Seminar: Sensor Data Compression and Power Management for Activity Recognition
The aim of the project is to design an energy-efficient context-aware strategy for activity recognition using sparse sampling and sensor data compression. Prototyping will be done in Python. Evaluation will be performed on free-living dataset and benchmarked in terms of energy/memory savings, time constraints and quality of information retrieved. Machine learning techniques will be applied for pattern recognition.
- Gain overview on the state-of-the-art of software-based sparse sampling and signal compression for wearable devices.
- Learn concepts of data compression.
- Learn concepts of sparse sampling.
|ECTS||2.5, 5, 7.5, default: 5|
|Project type||Seminar, Extension to BSc.MSc.-Thesis can be discussed|
|Presence time||4 SWS|
|Work distribution||25% Theory, 75% Programming|
|Useful knowledge||Python programming, machine learning basics|
|Period||Summer semester 2019|
|First meeting||Seminar introduction/Vorbesprechung on
24. Apr 2019, 17:00-18:30 at Henkestr. 91, Haus 7, 1. OG, R 373
|Med. Eng. Seminar Title||Advanced Context Recognition (ACR)|